Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

Karyotis, Konstantinos; Angelopoulou, Theodora; Tziolas, Nikolaos; Palaiologou, Evgenia; Samarinas, Nikiforos; Zalidis, George C.

doi:10.3390/land10010063

Cited by 13 publications

(11 citation statements)

References 65 publications

(80 reference statements)

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“…This could possibly be due to the data being randomly split between calibration and validation only once, while taking care that the similar range for soil property quantities remained common between the calibration and validation datasets ( Figure 5 ). However this possibility was not tested and could be tested in future by splitting the data many times and modeling the data using the five-fold cross-validation technique [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…The next step of this research would be to assess the portable mid-IR and vis-NIR soil spectra obtained under field conditions and further eliminate the environmental and systematic factors [ 44 , 63 ] affecting both soil spectra in a similar way to [ 46 , 47 , 72 ]. In addition, a variety of calibration models (ranging from simple to complex), e.g., partial least squares regression (PLSR) [ 1 , 73 , 74 ], principal component regression (PCR) [ 7 , 75 ], artificial neural networks (ANN) [ 76 ], convolution neural networks (CNN) [ 77 , 78 , 79 ], support vector machines (SVM) [ 80 ], and deep learning [ 81 , 82 , 83 , 84 ], to name a few, could be studied.…”

Section: Resultsmentioning

confidence: 99%

“…Soil moisture and other environmental factors affect the soil spectra recorded under field conditions [ 1 ]. In addition, other systematic factors such as viewing angle may also affect the end results [ 44 , 47 ]. For example, the authors in [ 44 ] analyzed the repeatability of vis-NIR and mid-IR soil spectral data obtained using different measurement techniques and found that several wavelengths of the spectra were highly repeatable whereas several parts of the wavelengths were less repeatable as well.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

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In contrast with classic bench-top hyperspectral (multispectral)-sensor-based instruments (spectrophotometers), the portable ones are rugged, relatively inexpensive, and simple to use; therefore, they are suitable for field implementation to more closely examine various soil properties on the spot. The purpose of this study was to evaluate two portable spectrophotometers to predict key soil properties such as texture and soil organic carbon (SOC) in 282 soil samples collected from proportional fields in four Canadian provinces. Of the two instruments, one was the first of its kind (prototype) and was a mid-infrared (mid-IR) spectrophotometer operating between ~5500 and ~11,000 nm. The other instrument was a readily available dual-type spectrophotometer having a spectral range in both visible (vis) and near-infrared (NIR) regions with wavelengths ranging between ~400 and ~2220 nm. A large number of soil samples (n = 282) were used to represent a wide variety of soil textures, from clay loam to sandy soils, with a considerable range of SOC. These samples were subjected to routine laboratory soil analysis before both spectrophotometers were used to collect diffuse reflectance spectroscopy (DRS) measurements. After data collection, the mid-IR and vis-NIR spectra were randomly divided into calibration (70%) and validation (30%) sets. Partial least squares regression (PLSR) was used with leave one out cross-validation techniques to derive the spectral calibrations to predict SOC, sand, and clay content. The performances of the calibration models were reevaluated on the validation set. It was found that sand content can be predicted more accurately using the portable mid-IR spectrophotometer and clay content is better predicted using the readily available dual-type vis-NIR spectrophotometer. The coefficients of determination (R2) and root mean squared error (RMSE) were determined to be most favorable for clay (0.82 and 78 g kg−1) and sand (0.82 and 103 g kg−1), respectively. The ability to predict SOC content precisely was not particularly good for the dataset of soils used in this study with an R2 and RMSE of 0.54 and 4.1 g kg−1. The tested method demonstrated that both portable mid-IR and vis-NIR spectrophotometers were comparable in predicting soil texture on a large soil dataset collected from agricultural fields in four Canadian provinces.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, compact size and portable sensors based on microelectromechanical systems (MEMS) are undergoing a significant shift [103], enabling among other applications the development of new innovative VNIR-SWIR sensing applications for soil properties or real time variable rate soil sensors [104]. In this context, a set of novel and low-cost spectral acquisition systems has been explored in conjunction with non-linear regression algorithms for predicting soil properties [105][106][107], mainly under controlled illumination conditions in the laboratory. Among other advances, augmented reality now also appears in soil mapping applications, where the locations for sampling are generated automatically, and the user is guided by special glasses to collect the samples representing management zones [108].…”

Section: Integration Of In Situ Sensing Systems and Citizen Science Datamentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

Self Cite

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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“…Obviously, spectrometers play an important role in spectral analysis [ 10 , 14 , 15 , 16 , 17 , 18 , 19 ]. During the past three years, many studies have investigated the influences of spectrometers on the spectral analysis of SOC ( Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

A Method to Evaluate Spectral Analysis by Spectroscopy

Liu

Fan

Qiu

et al. 2022

Sensors

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Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. The results show that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method.

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Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

Cited by 13 publications

References 65 publications

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

A Method to Evaluate Spectral Analysis by Spectroscopy

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